Video stabilization removes unwanted motion from video sequences, often caused by vibrations or other instabilities. This improves video viewability and can aid in detection and tracking in computer vision algorithms. We have developed a digital video stabilization process using scale-invariant feature transform (SIFT) features for tracking motion between frames. These features provide information about location and orientation in each frame. The orientation information is generally not available with other features, so we employ this knowledge directly in motion estimation. We use a fuzzy clustering scheme to separate the SIFT features representing camera motion from those representing the motion of moving objects in the scene. Each frame’s translation and rotation is accumulated over time, and a Kalman filter is applied to estimate the desired motion. Examples of video frames (BOOKSHELF Video) before and after stabilization are shown below.
This research was supported by grant IIP-1032047 from the National Science Foundation.
Related Publications:
- Kevin L. Veon, Mohammad H. Mahoor, Richard M. Voyles, ” Video Stabilization Using SIFT-ME Features and Fuzzy Clustering “, In the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), Sep. 2011, San Francisco, CA.